IADLC 05(International Advanced Digital Library Conference)

A UNIFIED FRAMEWORK FOR AUTOMATIC
METADATA EXTRACTIONFROM ELECTRONIC DOCUMENT
Asanee Kawtrakul, Chaiyakorn Yingsaeree and Team
NAiST Research Laboratory
Dept of Computer Engineering, Faculty of Engineering
Kasetsart University, THAILAND
26 August 2005, Nagoya

1Natural Language Processing and Intelligent Information System Technology Research Laboratory

Goal

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

Ontology

Multi-viewpoint Knowledge
Tracking
AccessKnowledge Tracking : Different Tracking Paths (Same Documents)
Collection &ComputerAcquisition

Smart
KU SearchSearchEngineEngineTracking by Domain, Title
Domain=Plant

Document
Image

NAiST
Thai OCR
OCRFA

E

B
DTexts2000 2002
13Services

Title=Ginger Title=Cabbage Title=CucumberAuthor
B MetadataC

Year

Delivering
Tracking byTitle, Author…
SystemTexts
Ontology

AC…

CF

A

D

B

2000E

Title=Cabbage

2001
2002
Author=Doae
Author=KU

Extraction Tools
Document
KW. Tracking Tracking by Author, TitleClustering
Author = KU
2004 Process
2001
2002
A
B
C

…

42Query

Computer

5Title=Ginger

14Title=Cabbage Title=Cucumber

2A

A

D

3C5Knowledge Gain :Another Knowledge Gain :Multimedia AnotherDatabase
F -AuthorA D CBis onlyC
E one who published in year 2001
-Author B is a new researcher.
(e.g.
DSpace)-Author A and B are pioneer researchers in domain.
-Author C publishes papers continuously
Posgres-And more ...
-Author A do not publish in year 2001
SQL
-And more...

DSpace3

Natural Language Processing and Intelligent Information System Technology Research Laboratory

Today’s Outline
Introduction
ProblemsArchitecture
Current Status
Conclusion
Ongoing Projects4

Natural Language Processing and Intelligent Information System Technology Research Laboratory

Introduction
 What is metadata?
Data about data
 Ex:



About document:Traditional library card catalogue,About content: purpose, problem spaces,methodologies, and results.
 Why is it important?
Help people distinguish relevant from non-relevant
documents,
 Multi-view point of Knowledge Tracking

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

Examples of Metadata Title
Some Meaning Procedures of Ontological Semantics

Authors

Marjorie McShane,Stephen Beale and Sergei Nirenburg

GraduatedStudent

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Institute of Language and Information Technologies
University of Maryland Baltimore Country{marge,sbeale,sergei}@umbc.edu

Affiliation

Systematic & Fixed order
GraduatedYear

Natural Language Processing and Intelligent Information System Technology Research Laboratory

Introduction (2) Where does it come from?
 By Human
Annotating the document manually By ComputerMetadata HarvestingMetadata Extraction

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

Introduction (3)Metadata HarvestingCollect metadata from previously defined
metadata
Usually performed by creating a parser to analyze
source metadata and transform parsing results intoan appropriated format
Application includes interoperability between
metadata of different systems and platforms

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

Introduction (4)Metadata ExtractionExtract metadata from document content
Usually performed by machine learning, rulebased parser and Regular Expression
Machine learning approaches are robust andadaptable, but require a large training example
Rule-based parsers and Regular Expression aredependent on an application domain, and notraining example is required

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Natural Language Processing and Intelligent Information System Technology Research LaboratoryIntroduction (5)ObjectiveCreate a framework for automatic metadata
extraction from technical and thesis
documents which have fixed format.

Solution
Use rule-based parser due to simplicity andcost
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Natural Language Processing and Intelligent Information System Technology Research Laboratory

ProblemsVariety of electronic document formats
E-Document can be stored in a variety of formats
e.g. Microsoft Word, Adobe Acrobat, Image of document, etc.

It is necessary to convert such document into text file inorder to access document content

Quality of extracted metadata
Extracted metadata may contain errors both from original
documents and text conversion process,Some mechanisms are required to produce high-quality
metadata

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

Architecture
Language Dictionary
Model

IDENTIFY&CORRECT ERRORS from- task-oriented parser-controlled vocabularies-general vocaburaries- existing metadata repository

Text
Text Conversion
Conversion
ModuleModuleTask-Oriented Parser
Task-OrientedParser
ModuleModuleAuthor’s Name
Thesis’s Title
DegreeMajor

12ExistingMetadata

Data
Data Verification
Verification
ModuleModuleCorrectedMetadata

Tanyaratana Dumka
Differential Expression of 1Amniocyclopropane-1-……
Master of Science
Field Genetic Engineering

Extracted
Metadata

Natural Language Processing and Intelligent Information System Technology Research Laboratory

Text Conversion Module (1)AFPL Ghostscript (for PS & PDF)
CATDOC (for Microsoft Word & Excel)
OCR (for Image Document)
Document Skew Correction
Marginal Noise Removal
Salt-and-Pepper Noise Removal
Broken Character Management

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

Text Conversion Module (2)Broken Character

Skew

Marginal Noise
Salt-and-Pepper Noise

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

Optical Character Recognition
 Conversion from Image to Text

Optical
OpticalCharacter
CharacterRecognition
Recognition

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หญาแฝกหอมหรือแฝกลุม 4 พันธุ
1. พันธุศรีลังกาดินลูกรัง
2. พันธุกําแพงเพชร 2ดินทรายถึงลูกรัง
3. พันธุสุราษฎรธานีดินรวนเหนียวถึงลูกรัง
4. พันธุสงขลา 3ดินรวนเหนียวถึง
ลูกรังNatural Language Processing and Intelligent Information System Technology Research Laboratory

Optical Character Recognition

Line Segmentation
Character Segmentation
Character Recognitionหญาแฝกหอมหรือแฝกลุม 4 พันธุ
1. พันธุศ รีลังกาดิน
ลูกรัง
2. พันธุก ําแพงเพชร 2ดินทรายถึงลูกรัง
3. พันธุส ุราษฎรธานีดิน
รวนเหนียวถึงลูกรังNatural Language Processing and Intelligent Information System Technology Research Laboratory
4 พันธสงขลา 3ดินText Generation

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Optical Character Recognition

Line Segmentation
Character Segmentation
Character Recognition
Text Generation

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

Optical Character Recognition

Line Segmentation
Character Segmentation
Character Recognition
Text Generation

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

Optical Character Recognition

Line Segmentation
Character Segmentation
Character Recognition
Text Generation

19ห

ญ า

Natural Language Processing and Intelligent Information System Technology Research Laboratory

Optical Character Recognition

Line Segmentation
Character Segmentation… ห อ ม ห ร อ แ ฝ ก ล ุCharacter Recognition
Text Generation20หญาแฝกหอมหรือแฝกลุม 4พันธุNatural Language Processing and Intelligent Information System Technology Research Laboratory

Optical Character Recognition

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

Automatic Metadata Extractionผูแตงนายวีร สัตยมาศตรชื
คําอเรื่องสําคัญการสรางดัชนีหนังสืออัตโนมัติการประมวลผลภาษาธรรมชาติการสรางดัชนี
…ฯลฯ...

Needs Resources and Cost

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

Extraction Meta Data for e-thesis
Graduate Year
Thesis Title
Student’s Name
Degree Name

Department Name
Advisor’s Degree

ISBN Number

Advisor’s Position
Advisor’s Name

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

Automatic Metadata Extraction
Tanyaratana Dumkua 2000

Task-OrientedParser<sentence><name>

Regular Expressions<sentence> :- <name> <year><name>:- <firstname> <lastname><firstname> :- [A-Z][a-z]+<lastname> :- [A-Z][a-z]+<year>:- [0-9]+<firstname><lastname><year>

Dumkua2000

tanyaratana

Firstname:

Tanyaratana

Lastname:

DumkuaYear:

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

2000Extraction Result for e-thesis

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Name:

อุบลวรรณSurname:นนทพันธุYear:

2543

Topic:

การเรงปฏิกิริยาการยอยสลายมูลฝอยเศษอาหารในกระบวนการหมักแบบไรออกซิเจน

Major:

วิทยาศาสตรสิ่งแวดลอมDepartment:โครงการสหวิทยาการระดับบัณฑิตศึกษา…

….

Natural Language Processing and Intelligent Information System Technology Research Laboratory

Data Verification Module (1)Error from Task-Oriented Parser Module
 Controlled Vocabularies
 General Vocabularies

Error in Existing Metadata Repository

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

Data Verification Module (2)Error from Task-Oriented Parser Module
The parser might not be able to parse somedocuments due to incomplete grammar,
error from text conversion, or defect in thedocument itself
To solve the problem, either creating newrules or fixing the defect is required

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

Data Verification Module (3)Error in Controlled Vocabularies
Some metadata fields’ value can be only a word(s) incontrolled vocabularies
Error identification can be achieved by comparing
extracted data with a dictionary
When error occurs, the correction process simply
replace the error word with its closest word in thedictionary by means of Edit Distance28

Natural Language Processing and Intelligent Information System Technology Research Laboratory

Data Verification Module (4)Error in General Vocaburaries
Use spelling correction technique todetect and correct the errors
 OCR Error Correction

 Typing Error Correction

This module is under development

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

Data Verification Module (5)Error in Existing Metadata Repository
Hand-made metadata usually contained
many errors
Instead of manually correcting the error, we
can use automatic metadata extraction andalignment tool to ease data correction
process

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

Current Status

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

Extracting metadata from students’ thesis abstract (1)32Natural Language Processing and Intelligent Information System Technology Research Laboratory

Extracting metadata from students’ thesis abstract (2)The preliminary results with 3,712 thesis
show that using this system greatly
reduce the labor work of metadata
creation process by correctly extracting
metadata 91.41% of the documents.

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

Extracting plant information from image of Thai
plant name dictionary(1)
Genus
name

Epithet’sauthor name

FamilySubfamily
nameSpecific
epithetPlant habits

EnglishpronunciationProvince
Thai name34

Natural Language Processing and Intelligent Information System Technology Research Laboratory

Extracting plant information from image of Thai
plant name dictionary (2)35Natural Language Processing and Intelligent Information System Technology Research Laboratory

Conclusion
A Unified Framework for Automatic Metadata
Extraction from Electronic Document
Consists of three main components
text conversion moduletask-oriented parser moduledata verification module

The experimental result shown that using theframework greatly reduce the labor work ofmetadata creation process

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Natural Language Processing and Intelligent Information System Technology Research LaboratoryOngoing Projects
Agricutural Knowledge Portal
Ontology MaintenanceInformation extractionKnowledge Mining

Open source Digital Library Knowledge collecting, sharing andAccessing (DSpace)Library System Management (Koha)

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

Real-World
Ontology

Unstructured,Semi-structured,
Structured
Document

Meta Data
Annotation tools

Diseases&Ontology
Howto
Task Oriented
protect?
Ontology

Thai AGRIS
CorpusHow toplant inthe winter?

etc.

Knowledge
Structure

Knowledge
RicePortal
Processing
Agricultural
Information Bases

Yield

Follow upIntelligent
the price Search Engine
Multilingual
Dictionary

MTKTWWW

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

Ontology Development forEnhancing Service
Task Oriented ontology
disease control

concepts

Treatment

cause

Plant Diseases
IS-A relation

cause from
pathogenspecific relations(e.g. Cause, hasSymptom)

cause fromenvironment

symptom...

...

BlightScorchinstances

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

Automatic Ontology Construction
System Architecture

Raw
Text
ExampleStructuredCorpus
AGROVOC
Thesaurus
ThaiPlant
Name
Dictionary

Unstructured CorpusผักกาดหอมThesaurus
Specific epithetRaw Text
DictionaryAuthorNameเปนผักจําพวกผักสลัดที่มี
ผักกาดหอมเปนผักที่ใชบริโภคสวนใบ
Family/Subfamilyคุณคาทางอาหารสูง นิยมบริโภคกันแพรหลายทีส่ ดุ ในบรรดาผักสลัด
GenusHabit
Morphologicalดวยกัน โดยสวนใหญนิยมรับประทานสดและนํ
Structure ามาประกอบอาหารหลายAnalysis
GESNERIACEAE
ชนิดChiritaคนไทยนิยมใชผกั กาดหอมกินAnalysis
กับอาหารจําพวกยําตางๆ สาคูหมู หรือDictionary
fulva
Barnett H ดาดหอย(Nakhon ขSiองผัThammarat).
ขาวเกรียบปากหมอ เปนตDathoiน ประโยชนกกาดหอมนอกจากจะใช
ิน
LocalกName
Characteristicน้ําดับไฟ NamงdapfaiThani);มะและ
involucrata
Databaseเปนผั
กสดที่มีคCraib
ุณคTerm
าHทางอาหารสูแลว ยั(Suratงจัดเปนอาหารทางตาดวยโดยการ
Malae (Pattani).
FormalConversion
นํามาตกแตงอาหารใหมีสสี ันสวยงามนารับประทานมากขึ้น นอกจากนี้ Name
micromusa B. Extraction
L. Burtt H คําหยาด Kham yat (Nakhon
Thesaurusผักกาดหอมยังมีคณุสมบัตใินการเปน
ยาอีกดวยความตองการผักกาดหอม
Ratchasima).
RecyclingมีอยูChisochetonต ลอดทั้งป โดยเฉพาะในชวงเทศกาลตางๆ จึงนับไดวาผักกาดหอมเปน
MELIACEAE
Semantic
Lexico-Syntactic
Pattern,ผักทีceramicus่มคี วามสํา
คัญRelationทางเศรษฐกิจชนิ
ดหนึ(General).
ง่ ที่นับวันจะทวีความตองการ(Miq.)
CDC. T ยมใหญ
YomyaiGrammarticalRules,
cumingianus(CDC.) Harms subsp. balansar (C.DC.) Mabb.T ยมเพิ่มขึ
้นเรื่อยๆ Identification
Heuristic Rules
Yom makokช(ChiangMai).
มะกอกผักกาดหอมมีื่อเรียกอื่นๆ ไดหลายชื่อเชน ภาคเหนือเรียกวา ผักกาดOntologyยี ภาคกลางเรียกวาผักสลัด เปนตน ผักกาดหอมเปนพืชทีจ่ ัดอยูในตระกูลCompositae มีชื่อวิทยาศาสตรวา Organizing
Lactuca sataiva Verificationมีถิ่นกําเนิดในทวีปSystemSystemเอเชียและยุโรป มีปลูกในประเทศไทยมาชานานแลวCereals

40BTNT

Plant Product
Oats
RiceMaize

Natural Language Processing and Intelligent Information System Technology Research Laboratory

Corpus based
Ontology Construction
Problems in this process:Many Candidate Terms
NP1... NP2... NP3... such as NP, NP, ...
Ex1.
herbs
can beused aswith
medicineand some
Ex2.
SunMany
floweris rather
enduring
dry season
whileof them
are manufactured
the industry
level,
such
as garlic,ginkgocomparing toinotherfield crops
such
as corn,soy beanbiloba.
and green bean.
CandidateTerms=> Sunflower, industry
field crop
CandidateTerms=> herbs,medicine,

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

Ontological Term Selection• Statistical Technique– Mutual Information, the measure of word association
MI (w1, w2) = log2

P(w1,w2)
P(w1) P(w2)Where
w1 is a candidate term
w2 is a related termP(wi) is probability of term wi
P(wi, wj) is probability of co-occurrence of term wi and wj– Example
Many herbs can be used as medicine and some of them are
manufactured in the industry level, such as garlic, ginkgo biloba
MI(herb, garlic) > MI(medicine, garlic), MI(industry, garlic)
Results : HYPO(garlic, herb)

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Natural Language Processing and Intelligent Information System Technology Research LaboratoryDictionary based Ontology Construction
OCRSystem
PrintedDictionary
Specific epithet
Genus

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Dictionary
in Electronic Format
Structure
Analysis

Author Name
Habit

Structured Corpus

Family/Subfamily

Dictionary
Characteristic•Technique: Database
Conversion
Appliedtask
Local Nameoriented parser to extract
Formal Name
relation
terms byalphabet characteristic
Forest Ontologyand position of terms

Natural Language Processing and Intelligent Information System Technology Research LaboratoryDictionary basedOntology Construction
Alphabet Characteristic of Dictionary.
FeatureDatabase field

ExampleAll upper case

Family/Sub-Family

EUPHORBIACEAE

Start with upper case

Genus

Acalypha

All lower caseSpecific epithetbrachystachya

Thai alphabet with bold
font

Formal Nameตําแยดอยใบบาง

Thai alphabet

Local Nameเกีย้ วเกลา Limitation:
 Dictionary has only plant names

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

AGROVOC Thesaurus
based Ontology Construction
 Technique: Convert BT/NT to IS-A Relation

Cereals BT Plant ProductNT Oats
Rice
Maize

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Plant Product
IS-A

Cereals
IS-AOats

IS-A

Rice

Natural Language Processing and Intelligent Information System Technology Research Laboratory

IS-A

Maize

Experimental Results
SourceNumber ofTerms

Number ofRelations

Accuracy

Raw Text (150 doc.)

3,720

3,312

73 %.

Dictionary

37,11021,620100%.

Thesaurus

27,54015,62891%.

3 Sources

43,07331,38787 %.

 By random checking with 1,000 united terms, the accuracy of the system is87 %.

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

47

Natural Language Processing and Intelligent Information System Technology Research LaboratoryDeployment
Knowledge portal as

One Stop Service

Better living condition ofAgriculture48

Natural Language Processing and Intelligent Information System Technology Research Laboratory

Finally, We have just initiated an open source Digital
Library since it will be the back bone of elearning for both formal and informal education.
Especially, for informal education, we should
thinking about extension to the root of grass suchas farmers and also organization – workers forbecoming Knowledge -workers.
This open source DL will be added moreadvanced features such as assistant tools forcollecting Knowledge, automatic cataloging,automatic indexing, information extraction andso on.

Knowledge based Society andEconomy,Acadamic Knowledge Factory andKnowledge Park

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Natural Language Processing and Intelligent Information System Technology Research Laboratory

Acknowledgement
KURDI: Kasetsart University Research andDevelopment InstituteGraduate School of Kasetsart University
IADLC2005 Chairs and Organizer

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Natural Language Processing and Intelligent Information System Technology Research Laboratory